Grounding every decision, model, and recommendation in peer-reviewed scientific evidence and validated clinical data. We prioritize reproducible results over speed.
Research & Development
Scientific
Scientific
Integrity.
"The greatest discoveries are often hidden in data, unit science finds the story they tell."
— John Tukey, 1962
Our Focus
Key Areas of Investigation
- From Prediction to Prevention – algorithms to forecast disease risk and optimise interventions.
- Explainable AI in Healthcare – making AI outputs transparent and clinically useful.
- Personal Care Pathways – tailoring treatment and lifestyle interventions for each patient.
- Multi-modal Data Integration – combining lifestyle, blood biomarkers and genetics.
Philosophy
At the Service of Medicine
At Ambr Institute, our research team combines medical expertise with advanced AI to tackle some of the world's most pressing health challenges. From early detection of dementia to personalized surgical recovery plans, our mission is to make precision medicine accessible, explainable, and effective for every patient.
We believe in healthy aging for all.
Methodology
Scientific Principles
Evidence-based Practice
Objectivity
We design our algorithms to minimize bias. Data interpretation is separated from commercial interests, ensuring that our clinical recommendations are driven solely by patient outcomes and biological reality.
Collaboration
Medicine is a team sport. We actively partner with academic institutions, clinical practices, and patient advocacy groups to ensure our tools solve real-world problems faced by doctors and patients today.
Ethical Responsibility
We adhere to the strictest standards of data privacy and patient consent. Our AI is designed to support the doctor-patient relationship, not replace it, ensuring human oversight at every critical juncture.
Ongoing Studies
Current Projects & Collaborations
Deployment Status: Active
Location: Norway / EU
AI Assisted Diagnostics for Neuroendocrine Carcinomas
Developing multi-modal deep learning models to identify early markers of neuroendocrine carcinomas in routine blood work and imaging data, aiming to reduce diagnosis time by up to 40%.
Prediction of Surgical Outcomes & Personalised Prehabilitation
Utilizing longitudinal patient data to predict surgical recovery trajectories. This project creates tailored "prehabilitation" protocols (nutrition and exercise) to optimize patient readiness before operation.
Alzheimer Disease and Dementia
A collaborative longitudinal study focusing on the interaction between metabolic health, sleep patterns, and early cognitive decline to establish new preventative screening protocols.
Explainability in AI for Clinical Use
Developing a proprietary XAI framework that provides clinicians with natural-language reasoning behind every algorithmic risk score, ensuring trust and regulatory compliance.
Supported by
Clinical Research
Ongoing Clinical Trials
Frontiers in Digital Health
The dHOPE Study
Digital home-based multimodal prehabilitation of colorectal cancer patients prior to surgery: a non-inferiority clinical trial protocol
The dHOPE study is a three-armed, open-labelled, parallel-group randomized controlled trial (RCT) with a non-inferiority design to compare a digital home-based prehabilitation program with a hospital-based program or no organized prehabilitation. In addition, the trial aims to identify measurable parameters reflecting the effect of prehabilitation, preparing for future personalization of prehabilitation programs.